A deep learning framework for skin lesion classification quantified diagnostic uncertainty and calibrated predicted probabilities on the HAM10000 benchmark. The approach combined DINOv2 representations with Monte Carlo Dropout and temperature scaling to better gauge uncertainty rather than output fixed confidence scores, aligning model outputs more closely with real-world diagnostic risk assessment needs.